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Deep Residual CNN Based Model for Human Activity Recognition System

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dc.contributor.author Tareque, Saifuddin Mohammad
dc.date.accessioned 2019-09-29T10:40:21Z
dc.date.available 2019-09-29T10:40:21Z
dc.date.issued 2019-05-26
dc.identifier.uri http://hdl.handle.net/123456789/3480
dc.description.abstract Human Action Recognition (HAR) is a significant application realm in computer vision, but high precision recognition of human action in the complex background is still an open question. Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In our research, as classifier, a deep Convolutional Neural Network (CNN) using ResNet-50 model is proposed for HAR because it is the most upper hand in compare to other classifiers. Our proposed research work have used publicly accessible UCF-101 dataset which provides the largest multiplicity in HAR filed as most of the available action recognition data sets are not realistic. Additionally, UCF-101 dataset intends to give support further research into action recognition by learning and surveying new pragmatic action categories. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.subject Human Action Recognition en_US
dc.subject Computer Science en_US
dc.title Deep Residual CNN Based Model for Human Activity Recognition System en_US
dc.type Other en_US

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